Search Results for author:
Found 10 papers, 6 papers with code
Graph-based Untrained Neural Network Detector for OTFS Systems
Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments.
See SIFT in a Rain
One is difference of Gaussian (DoG) pyramid recovery network (DPRNet) for SIFT detection, and the other gradients of Gaussian images recovery network (GGIRNet) for SIFT description.
Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems
In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
Bag of Tricks and a Strong Baseline for FGVC
This motivates us to explore the impact of different methods and components in fine-grained classification on FungiCLEF 2022.
A viable framework for semi-supervised learning on realistic dataset
Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied.
Mining limited data for more robust and generalized ML models
It’s more efficient to look for ways improving the data based a fixed neural network architecture.
BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition
Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
Skin cancer reorganization and classification with deep neural network
Here we report a novel strategy based on deep learning technique, and achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1) we build a segmentation neural network (skin_segnn), which achieved very high lesion boundary detection accuracy; 2) We build another very deep neural network based on Google inception v3 network (skin_recnn) and its well-trained weight.
